English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Aspect based sentiment analysis (ABSA) deals with the identification of thesentiment polarity of a review sentence towards a given aspect. Deep Learningsequential models like RNN, LSTM, and GRU are current state-of-the-art methodsfor inferring the sentiment polarity. These methods work well to capture thecontextual relationship between the words of a review sentence. However, thesemethods are insignificant in capturing long-term dependencies. Attentionmechanism plays a significant role by focusing only on the most crucial part ofthe sentence. In the case of ABSA, aspect position plays a vital role. Wordsnear to aspect contribute more while determining the sentiment towards theaspect. Therefore, we propose a method that captures the position basedinformation using dependency parsing tree and helps attention mechanism. Usingthis type of position information over a simple word-distance-based positionenhances the deep learning model's performance. We performed the experiments onSemEval'14 dataset to demonstrate the effect of dependency parsingrelation-based attention for ABSA.

ATP: A holistic attention integrated approach to enhance ABSA